This paper proposes a new classification ensemble model which is based on feature selection.
本文提出了一种基于特征提取的分类集成模型。
参考来源 - 基于特征提取的分类集成在脾虚证诊断中的应用·2,447,543篇论文数据，部分数据来源于NoteExpress
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。
Compared with the single suppo vector machine method, the support vector machine ensemble method has better classification accuracy.
模拟实验结果表明，该方法具有明显优于单一支持向量机的更高的分类准确率。
To resolve combining classifiers decisions among ensemble classification over data streams without labeled examples, a transductive constraint-based learning strategy was proposed.
为了解决在没有已知标签样本的情况下数据流组合分类决策问题，提出一种基于约束学习的数据流组合分类器的融合策略。
应用推荐